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Volumn 2006, Issue , 2006, Pages 897-904

Full bayesian network classifiers

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; DECISION THEORY; ENCODING (SYMBOLS); LEARNING ALGORITHMS; MATHEMATICAL MODELS; TREES (MATHEMATICS);

EID: 33749239833     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (74)

References (20)
  • 1
    • 0036567524 scopus 로고    scopus 로고
    • Learning Bayesian networks from data: An information-theory based approach
    • Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W. (2002). Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence Journal, 137:1-2, 43-90.
    • (2002) Artificial Intelligence Journal , vol.137 , Issue.1-2 , pp. 43-90
    • Cheng, J.1    Greiner, R.2    Kelly, J.3    Bell, D.4    Liu, W.5
  • 3
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • Cooper, G., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
    • (1992) Machine Learning , vol.9 , pp. 309-347
    • Cooper, G.1    Herskovits, E.2
  • 8
    • 0002370418 scopus 로고    scopus 로고
    • A tutorial on learning with Bayesian networks
    • M. I. Jordan (Ed.), MIT Press
    • Heckerman, D. (1999). A tutorial on learning with Bayesian networks. In M. I. Jordan (Ed.), Learning in Graphical Models, 301-354. MIT Press.
    • (1999) Learning in Graphical Models , pp. 301-354
    • Heckerman, D.1
  • 9
    • 34249761849 scopus 로고
    • Learning Bayesian networks: The combination of knowledge and statistical data
    • Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20, 197-243.
    • (1995) Machine Learning , vol.20 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 10
    • 0000545946 scopus 로고    scopus 로고
    • Improvements to Platt's SMO algorithm for SVM classifier design
    • Keerthi, S., Shevade, S., Bhattacharyya, C., & Murthy, K. (2001). Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation, 13(3), 637-649.
    • (2001) Neural Computation , vol.13 , Issue.3 , pp. 637-649
    • Keerthi, S.1    Shevade, S.2    Bhattacharyya, C.3    Murthy, K.4
  • 12
    • 0028482006 scopus 로고
    • Learning Bayesian belief networks: An approach based on the MDL principle
    • Lam, W., & Bacchus, F. (1994). Learning Bayesian belief networks: an approach based on the MDL principle. Computational Intelligence, 10(4), 269-293.
    • (1994) Computational Intelligence , vol.10 , Issue.4 , pp. 269-293
    • Lam, W.1    Bacchus, F.2
  • 14
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • MIT Press
    • Platt, J. C. (1998). Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods-Support Vector Learning. MIT Press.
    • (1998) Advances in Kernel Methods-support Vector Learning
    • Platt, J.C.1
  • 16
    • 0042346121 scopus 로고    scopus 로고
    • Tree induction for probability-based ranking
    • Provost, F. J., & Domingos, P. (2003). Tree induction for probability-based ranking. Machine Learning, 52(3), 199-215.
    • (2003) Machine Learning , vol.52 , Issue.3 , pp. 199-215
    • Provost, F.J.1    Domingos, P.2
  • 19
    • 14844351034 scopus 로고    scopus 로고
    • Not so naive Bayes: Aggregating one-dependence estimators
    • Webb, G. I., Boughton, J., & Wang, Z. (2005). Not so naive Bayes: Aggregating one-dependence estimators. Journal of Machine Learning, 58(1), 5-24.
    • (2005) Journal of Machine Learning , vol.58 , Issue.1 , pp. 5-24
    • Webb, G.I.1    Boughton, J.2    Wang, Z.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.